The goal of this project is to develop the candidate's ability to perform multidisciplinary research in neuroimage analysis, with emphasis on multivariate neuroimage classification and its application to computer aided early detection and diagnosis of Alzheimer's disease (AD). The research project will focus on the development of novel neuroimage classification algorithms for accurately identifying and measuring brain abnormality in a fully automatic and integrated way. The candidate will develop a general integrated neuroimage classification framework by integrating feature extraction, feature selection, and classification. Within this general framework, a multimodality pattern classification method with improved feature extraction from both structural and functional images will be developed. These multimodality multivariate classification methods will be validated and applied to the neuroimage based studies of early AD diagnosis and longitudinal measurement of brain abnormality related to AD. As part of the proposed KOI application, the candidate seeks didactic training in medical imaging, neuroscience, clinical diagnosis of AD, and biostatistics. The proposed training and research plan will foster the candidate's development into an independent scientist, using neuroimaging and image analysis methods for early detection and diagnosis of Alzheimer's disease. RELEVANCE: The improved neuroimage classification methods will help early detection and diagnosis of Alzheimer's disease. The release of the fully automatic neuroimage classification software will significantly improve the interoperability and adoptability of high dimensional pattern classification algorithms for neuroimage analysis, and result in enhanced dissemination, adoption, and evolution of such tools and resources by the broader neuroimaging research community.